Add to ORKGFederated learning is an emerging concept in the domain of distributed machine learning. This concept has enabled GANs to benefit from the rich distributed training data while preserving privacy However,in a non-iid setting, current federated GAN architectures are unstable, struggling to learn the distinct features and vulnerable to mode collapse. In this paper, we propose a novel architecture MULTIFLGAN to solve the problem of low-quality images, mode collapse and instability for non-iid datasets. Our results show that MULTI-FLGAN is four times as stable and performant (i.e. high inception score) on average over 20 clients compared to baseline FLGAN.CSE3000 Research ProjectComputer Science and Engineerin
With growing concerns regarding data privacy and rapid increase in data volume, Federated Learning(F...
The parallel growth of contemporary machine learning (ML) technologies alongside edge/-fog networkin...
For deep learning applications, the massive data development (e.g., collecting, labeling), which is ...
International audienceA recent technical breakthrough in the domain of machine learning is the disco...
International audienceExisting approaches to distribute Generative Adversarial Networks (GANs) eithe...
Machine learning makes multimedia data (e.g., images) more attractive, however, multimedia data is u...
We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and it...
Federated learning (FL) is an emerging machine learning technique where machine learning models are ...
© 2019 Sukarna BaruaGenerative Adversarial Networks (GANs) are a powerful class of generative models...
Generative adversarial networks have shown promise in generating images and videos. However, they su...
In recent years, Federated Learning has attracted much attention because it solves the problem of da...
Federated learning (FL) is a distributed machine learning paradigm that enables a large number of cl...
International audienceA recently celebrated kind of deep neural networks is Generative Adversarial N...
Applying knowledge distillation to personalized cross-silo federated learning can well alleviate the...
Federated learning (FL) emerges as a popular distributed learning schema that learns a model from a ...
With growing concerns regarding data privacy and rapid increase in data volume, Federated Learning(F...
The parallel growth of contemporary machine learning (ML) technologies alongside edge/-fog networkin...
For deep learning applications, the massive data development (e.g., collecting, labeling), which is ...
International audienceA recent technical breakthrough in the domain of machine learning is the disco...
International audienceExisting approaches to distribute Generative Adversarial Networks (GANs) eithe...
Machine learning makes multimedia data (e.g., images) more attractive, however, multimedia data is u...
We propose MAD-GAN, an intuitive generalization to the Generative Adversarial Networks (GANs) and it...
Federated learning (FL) is an emerging machine learning technique where machine learning models are ...
© 2019 Sukarna BaruaGenerative Adversarial Networks (GANs) are a powerful class of generative models...
Generative adversarial networks have shown promise in generating images and videos. However, they su...
In recent years, Federated Learning has attracted much attention because it solves the problem of da...
Federated learning (FL) is a distributed machine learning paradigm that enables a large number of cl...
International audienceA recently celebrated kind of deep neural networks is Generative Adversarial N...
Applying knowledge distillation to personalized cross-silo federated learning can well alleviate the...
Federated learning (FL) emerges as a popular distributed learning schema that learns a model from a ...
With growing concerns regarding data privacy and rapid increase in data volume, Federated Learning(F...
The parallel growth of contemporary machine learning (ML) technologies alongside edge/-fog networkin...
For deep learning applications, the massive data development (e.g., collecting, labeling), which is ...